This study introduces a Bayesian logistic regression framework that is capable of providing skillful probabilistic forecasts of Arctic sea ice cover, along with quantifying the attendant uncertainties. The presence or absence of ice (absence defined as ice concentration below 15%) is modeled using a categorical regression model, with atmospheric, oceanic, and sea ice covariates at 1‐ to 7‐month lead times. The model parameters are estimated in a Bayesian framework, thus enabling the posterior predictive probabilities of the minimum sea ice cover and parametric uncertainty quantification. The model is fitted and validated to September minimum sea ice cover data from 1980 through 2018. Results show overall skillful forecasts of the minimum sea ice cover at all lead times, with higher skills at shorter lead times, along with a direct measure of forecast uncertainty to aide in assessing the reliability.

中文翻译：

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贝叶斯Logistic回归用于最小北极北极9月冰盖概率预报

这项研究引入了贝叶斯逻辑回归框架，该框架能够提供北极海冰覆盖的熟练概率预测，并量化随之而来的不确定性。使用分类回归模型对是否存在冰（定义为冰浓度低于15％）进行建模，其中大气，海洋和海冰的协变量为1到7个月的交货时间。在贝叶斯框架中估计模型参数，从而实现最小海冰覆盖和参数不确定性量化的后验预测概率。该模型已拟合并验证了1980年至2018年9月的最低海冰覆盖率数据。结果显示，在所有交货时间均能熟练预测最低海冰覆盖率，而在较短的交货时间中具有较高的技能，